Automatic registration of landmarks in a medical image is usually performed by performing a computing method on a computer. Examples of such methods use an algorithm adjusted manually for detection of specific anatomic structures or points (landmarks). In other methods, the approach is based on machine learning for construction of a detector, which is capable of providing detection of position of anatomic structures or points. A typical method for registration of landmarks in an image includes scanning of an image by using a window and classifying window content according to a position for the purpose of investigation of a position of an anatomic structure. Various known algorithms can be applied for the solution of a task of classification of such methods, for example, using Markovian networks, using boosting, or extracting features of a general purpose (such as Haar's features).
A first related art technique involves a method of registering anatomic structures using machine learning. The main idea of this related art method is that the cascade of classifiers is applied to selected fragments of an image for verification of the presence of some anatomic points (landmarks) in a concrete position. Classifiers use the various fixed spatial features. The final procedure of classification is constructed using a boosting method. After classification, all candidates go through the procedure of verification which is performed using spatial statistical information taken from a learning sample. In addition, candidates are filtered based on their quality using fixed thresholds. Feature extraction algorithm is adjusted using guided learning.
A second related art technique involves a method of automatically registering landmarks using a combined context. The combined context refers to the set of features of combinations of some landmarks constructed by possible positions of separately taken landmarks. In the first stage, formation of candidates for landmarks is performed by application of learning in the limited subspace and using of a probability boosting tree. In the method, the position, orientation and scale are estimated according to the following steps: a) a trained classifier is applied for estimation of a position, b) a trained classifier is applied for estimation of a position and orientation, and c) a trained classifier is applied for estimation of a scale. Haar's features are used as low-level features. Selection of the best candidates for landmarks is performed by taking into account probability of occurrence of each separate point and the probability corresponding to a combined context of some combination of landmarks (for example, a pair of points in case it is necessary or desired to find landmarks of two types). The second related art technique is discloses in a patent for registration of a heart cap and basal plane on magnetic resonance images of heart.
A third related art technique involves a method of registering landmarks, which is similar to the second related art technique, except that, in the third related art technique, a geometrical model considering relative positions of landmarks is additionally used. An initial position of a first landmark is estimated by using the above described method using learning in a limited subspace. Then, a geometrical model, learned in marked medical images, is assigned to a position of the first landmark. Thus, search zones of other types of anatomic points are set. Positions of other landmarks are determined inside zones obtained by the method described above using learning in a limited subspace. Classification is performed using a probability boosting tree. An applicable field for the third related art technique is magnetic resonance images of a brain.
A fourth related art technique involves a method of automatically registering landmarks in 3D medical images. In the method, the registration is performed by selection of candidates for landmarks and construction of binary connections between the selected candidates. Registration of candidates is performed by using classification of features formed based on spatial histograms. Then, determination of the best candidates is performed by application of a model of Markovian networks to a related set of candidates.
Drawbacks of these methods are as follows. Most algorithms are capable of working with only two-dimensional medical images. Accordingly, such algorithms are applicable to a limited field. Some methods operate with only high-quality medical images of high resolution which is generated for a long period of time. Practically, in all of these methods, feature extraction algorithm is applied for a solution of a classification task. Generally, feature extraction algorithm is adjusted manually using the empirical information. In specific cases, trained models of feature extraction algorithm having a general purpose are used without taking into account specificity of the used data. In such situations, the potential of the approach of machine learning is not fully realized. Also, only one level of features is constructed frequently, instead of constructing a multilevel hierarchy of features. In some approaches, candidates for landmarks are filtered by using corresponding thresholds, however, such thresholds are also adjusted manually as a rule.